Unleashing Business Growth: Navigating Digital Transformation and AI Integration in Consumer Goods Industries
Vijay, an accomplished executive with over 19 years of experience in the Consumer Goods and Energy sectors, currently heads Business Transformation and Digital at Godrej Consumer Products Limited. With a robust background from roles at Shell and Unilever, he excels in leading diverse teams to drive innovation and digitalization strategies. As Global Chief Information and Digital Officer at Shell's Lubricants division, Vijay pioneered transformative initiatives to enhance customer engagement and route-to-market capabilities. He holds an MBA from IIM Lucknow and a Bachelor of Engineering from the College of Engineering Guindy, Chennai.
In conversation with siliconIndia magazine, Vijay Kannan expressed his thoughts on how the latest digital trends, such as RPA, analytical AI and generative AI, coupled with the integration of AI and machine learning, data analytics, and the collaboration between established companies and startups, are transforming industries and promoting innovation in business operations.
Latest Digital Trends and Innovations Shaping Industries
In today's fast-changing digital landscape, there are several key trends that are reshaping industries and revolutionizing business operations. One of the most significant trends is Robotic Process Automation (RPA), which has been around for a while but is becoming increasingly important. With the emergence of newer technologies, the deployment of RPA and the types of use cases that it can handle have grown rapidly. For example, multi-step complex automation with an element of decision-making in between those steps was not a typical use case before, but now it has become more common. RPA has become a pivotal trend due to the increasing sophistication of intelligent automation and core basic process automation. Another important trend is Analytical AI, which has always been there but is growing with the explosion of data. Organizations are striving to unlock the power of data through better analytical models, algorithmic modeling, machine learning, etc., to derive meaningful insights and value. This trend is becoming more and more critical for the success of businesses. The third trend is generative AI, which is still in its early stages. While there is a lot of hype and buzz around it, it has not yet delivered significant outcomes and value for organizations. However, there are some areas such as customer engagement or internal content management use cases that could benefit from generative AI. For it to become broad-based like Analytical AI, it may take a few more years as the cost of implementing generative AI needs to come down dramatically to make it commercially viable for organizations.
Identifying Opportunities for AI and Machine Learning Integration
As mentioned earlier, analytical AI, which involves machine learning, is helpful in identifying patterns in data and responding accordingly. For instance, in a manufacturing setting, you can use data collected from various machines on the shop floor to run analytical and predictive models using machine learning algorithms. This will help you understand the performance of the machines and any outliers that may indicate a problem with their health. By doing this, you can proactively take preventive measures such as performing maintenance or shutting down the machines to avoid potential issues that could reduce your overall throughput or efficiency. Another example of how AI and machine learning can drive operational efficiency is in customer experience. While Generative AI is still in its infancy, there are already use cases where it is being used to have more context-driven conversations with customers. Unlike previous chatbots which were limited to question-answer interactions, Generative AI allows for more natural and human-like conversations with customers. This is becoming a popular use case in many organizations for deploying AI technology.
Harnessing Data Analytics and Big Data for Informed Decision-Making and Process Optimization
The previous discussion mentioned how big data and algorithms could be utilized to enhance operational efficiency in the manufacturing industry. Similarly, we can also use these tools to improve demand planning in the supply chain. By analyzing a range of data sets, which were previously hard to acquire, such as weather patterns, seasonal trends, and e-commerce data, we can forecast consumer demand with greater accuracy. With the advancement in computing power and algorithms, we can achieve more precise and reliable outcomes. By improving the accuracy of our forecasting, we can reduce costs while enhancing customer service.
Challenges and Strategic Initiatives in E-commerce Value Chains
In the world of CPG manufacturing, there are several partners involved in the value chain, such as e-commerce retailers, modern trade, or distributors. Each player aims to provide the best value to their customers. For instance, distributors focus on serving the right retailers by providing them with the right products at the right time, with the right pricing structure, and incentives to maximize their profit. Technology and data can help in understanding the shopper and consumer preferences in specific areas, allowing distributors to determine the appropriate product portfolio that they need to hold. By providing the salesmen with nudges and guidance tips, they can have more effective and engaging conversations with retailers, which can lead to better sales. Ultimately, this approach benefits both the CPG manufacturer and the entire value chain.
Fostering Innovation through Collaboration between Startups and Established Companies
It's important for established companies to partner with startups to solve relevant customer and consumer problems in the market. Startups offer a chance to experiment with innovative problem-solving approaches, as not all answers may be readily available. These capabilities would take us months or even years to develop internally, but startups already have them in the external ecosystem, which we can shape and polish for value creation and relevant use cases. This partnership can drive speed, reduce the total cost of innovation, and help solve customer problems. Large organizations have a lot to offer, and startups can make their product market fit more relevant and learn real business outcomes. In turn, startups get guidance and direction to take their product forward. It's a symbiotic relationship that can benefit both large organizations and startups.
In conversation with siliconIndia magazine, Vijay Kannan expressed his thoughts on how the latest digital trends, such as RPA, analytical AI and generative AI, coupled with the integration of AI and machine learning, data analytics, and the collaboration between established companies and startups, are transforming industries and promoting innovation in business operations.
Latest Digital Trends and Innovations Shaping Industries
In today's fast-changing digital landscape, there are several key trends that are reshaping industries and revolutionizing business operations. One of the most significant trends is Robotic Process Automation (RPA), which has been around for a while but is becoming increasingly important. With the emergence of newer technologies, the deployment of RPA and the types of use cases that it can handle have grown rapidly. For example, multi-step complex automation with an element of decision-making in between those steps was not a typical use case before, but now it has become more common. RPA has become a pivotal trend due to the increasing sophistication of intelligent automation and core basic process automation. Another important trend is Analytical AI, which has always been there but is growing with the explosion of data. Organizations are striving to unlock the power of data through better analytical models, algorithmic modeling, machine learning, etc., to derive meaningful insights and value. This trend is becoming more and more critical for the success of businesses. The third trend is generative AI, which is still in its early stages. While there is a lot of hype and buzz around it, it has not yet delivered significant outcomes and value for organizations. However, there are some areas such as customer engagement or internal content management use cases that could benefit from generative AI. For it to become broad-based like Analytical AI, it may take a few more years as the cost of implementing generative AI needs to come down dramatically to make it commercially viable for organizations.
Identifying Opportunities for AI and Machine Learning Integration
As mentioned earlier, analytical AI, which involves machine learning, is helpful in identifying patterns in data and responding accordingly. For instance, in a manufacturing setting, you can use data collected from various machines on the shop floor to run analytical and predictive models using machine learning algorithms. This will help you understand the performance of the machines and any outliers that may indicate a problem with their health. By doing this, you can proactively take preventive measures such as performing maintenance or shutting down the machines to avoid potential issues that could reduce your overall throughput or efficiency. Another example of how AI and machine learning can drive operational efficiency is in customer experience. While Generative AI is still in its infancy, there are already use cases where it is being used to have more context-driven conversations with customers. Unlike previous chatbots which were limited to question-answer interactions, Generative AI allows for more natural and human-like conversations with customers. This is becoming a popular use case in many organizations for deploying AI technology.
Harnessing Data Analytics and Big Data for Informed Decision-Making and Process Optimization
The previous discussion mentioned how big data and algorithms could be utilized to enhance operational efficiency in the manufacturing industry. Similarly, we can also use these tools to improve demand planning in the supply chain. By analyzing a range of data sets, which were previously hard to acquire, such as weather patterns, seasonal trends, and e-commerce data, we can forecast consumer demand with greater accuracy. With the advancement in computing power and algorithms, we can achieve more precise and reliable outcomes. By improving the accuracy of our forecasting, we can reduce costs while enhancing customer service.
Challenges and Strategic Initiatives in E-commerce Value Chains
In the world of CPG manufacturing, there are several partners involved in the value chain, such as e-commerce retailers, modern trade, or distributors. Each player aims to provide the best value to their customers. For instance, distributors focus on serving the right retailers by providing them with the right products at the right time, with the right pricing structure, and incentives to maximize their profit. Technology and data can help in understanding the shopper and consumer preferences in specific areas, allowing distributors to determine the appropriate product portfolio that they need to hold. By providing the salesmen with nudges and guidance tips, they can have more effective and engaging conversations with retailers, which can lead to better sales. Ultimately, this approach benefits both the CPG manufacturer and the entire value chain.
Fostering Innovation through Collaboration between Startups and Established Companies
It's important for established companies to partner with startups to solve relevant customer and consumer problems in the market. Startups offer a chance to experiment with innovative problem-solving approaches, as not all answers may be readily available. These capabilities would take us months or even years to develop internally, but startups already have them in the external ecosystem, which we can shape and polish for value creation and relevant use cases. This partnership can drive speed, reduce the total cost of innovation, and help solve customer problems. Large organizations have a lot to offer, and startups can make their product market fit more relevant and learn real business outcomes. In turn, startups get guidance and direction to take their product forward. It's a symbiotic relationship that can benefit both large organizations and startups.
